Energy efficient workload placement management using predetermined server efficiency data

ABSTRACT

Additional workloads are assigned among servers in a power-efficient manner. For each of a plurality of servers, a stored power efficiency/capacity utilization relationship is accessed, current component power consumption values are obtained, and a current power consumption efficiency is calculated. An amount of capacity utilization necessary to perform an additional workload is obtained, and a predicted power consumption efficiency is determined for each server. The predicted efficiency is determined using the current power consumption efficiency of the server and the stored relationship. The workload is then assigned to the server that would have the greatest improvement in power consumption efficiency.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.15/057,829 filed on Mar. 1, 2016, which application is incorporated byreference herein.

BACKGROUND Field of the Invention

The present invention relates to methods of assigning workload instancesamong a plurality of servers.

Background of the Related Art

The power and cooling demands of large computing systems continue toincrease. Data centers are currently being challenged to both sustainthe increasing information technology (IT) demand, as well as to reduceenergy spending and carbon footprint. Today, data centers consume 3-5%of the total world energy consumption. Of this amount, at least 35% isassociated with data center cooling management, 10% is consumed by powerdistribution units, 5% is the overhead and the remaining 50% is thecompute power. Furthermore, rack level power demand is expected toincrease by 10-20% between 2015 and 2020. In some implementations, racksare already being partially populated for this very reason.

Given these power trends, there has been tremendous focus in trying tooptimize data center designs to recover rack space for IT and saveenergy. In order to achieve this, much attention has been given to thedesign of the mechanical systems such as chillers, free air economizers,high efficiency power distribution, and premium power supplies units.Further reductions in energy consumption and efficiency may be achievedby improving the individual components of the servers and other ITequipment.

Inside a typical air-cooled server, heat is generated mainly by the CPU,but also by the power supplies, memory, data storage device, networkingcontroller, core chipset, and so on. As the amount of heat goes up, thefans have to run faster to remove that heat, but the relationship is notlinear between power input to the fans and how effectively heat isremoved. In fact, where the amount of heat energy removed increaseslinearly, the power to remove it increases by the cube.

Since an idle server still consumes power, some methods of powerconservation will consolidate workload from several lightly-loadedservers to one heavily loaded server, and then suspend or shut-down theidled servers. However, this method requires suspend/resume capabilitybe enabled, which is rarely something datacenter operators are willingto do. Furthermore, this method is also based on the mistaken assumptionthat the few heavily loaded servers are operating efficiently.

BRIEF SUMMARY

One embodiment of the present invention provides a method of assigningworkload among servers in a power efficient manner. The method comprisesaccessing, for each of a plurality of servers, a previously storedrelationship between power consumption efficiency, capacity utilizationand ambient air temperature; obtaining, for each of the plurality ofservers, a plurality of current component power consumption values; andcalculating, for each of the plurality of servers, a current powerconsumption efficiency using the plurality of current component powerconsumption values. The method further comprises obtaining an amount ofcapacity utilization necessary to perform the workload. Still further,the method includes determining, for each of the plurality of servers, apredicted power consumption efficiency by identifying a current capacityutilization that the stored relationship associates with the currentpower consumption efficiency, calculating a predicted capacityutilization value by adding the amount of capacity utilization necessaryto perform the workload to the current capacity utilization, andidentifying the predicted power consumption efficiency that the stored)relationship associates with the predicted capacity utilization value.The method further comprises determining, for each of the plurality ofservers, any improvement in power consumption efficiency between thecurrent power consumption efficiency and the predicted power consumptionefficiency, identifying one of the plurality of servers that wouldexperience the greatest improvement in power consumption efficiency, andassigning the workload to the identified server.

Another embodiment of the present invention provides a computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, wherein the program instructions areexecutable by a processor to cause the processor to perform a method ofassigning workload among servers in a power efficient manner. The methodcomprises accessing, for each of a plurality of servers, a previouslystored relationship between power consumption efficiency, capacityutilization and ambient air temperature; obtaining, for each of theplurality of servers, a plurality of current component power consumptionvalues; and calculating, for each of the plurality of servers, a currentpower consumption efficiency using the plurality of current componentpower consumption values. The method further comprises obtaining anamount of capacity utilization necessary to perform the workload. Stillfurther, the method includes determining, for each of the plurality ofservers, a predicted power consumption efficiency by identifying acurrent capacity utilization that the stored relationship associateswith the current power consumption efficiency, calculating a predictedcapacity utilization value by adding the amount of capacity utilizationnecessary to perform the workload to the current capacity utilization,and identifying the predicted power consumption efficiency that thestored relationship associates with the predicted capacity utilizationvalue. The method further comprises determining, for each of theplurality of servers, any improvement in power consumption efficiencybetween the current power consumption efficiency and the predicted powerconsumption efficiency, identifying one of the plurality of servers thatwould experience the greatest improvement in power consumptionefficiency, and assigning the workload to the identified server.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a diagram of a previously determined and stored relationshipbetween power consumption efficiency and ambient temperature for variouslevels of capacity utilization.

FIG. 2 is a diagram of the previously determined and stored relationshipbetween power consumption efficiency and capacity utilization forvarious ambient temperatures.

FIG. 3 is a diagram of a computing system in which a management serverassigns workloads to a plurality of servers according to variousembodiments of the present invention.

FIG. 4 is a flowchart of a method according to one embodiment of thepresent invention.

FIG. 5 includes graphs of the previously stored power consumptionefficiency relationships for each of three servers currently operatingat different levels of capacity utilization.

FIG. 6 is a graph illustrating current power consumption efficiencymeasurements compared to the previously stored power consumptionefficiency relationships under three identical workload conditions.

DETAILED DESCRIPTION

One embodiment of the present invention provides a method of assigningworkload among servers in a power efficient manner. The method comprisesaccessing, for each of a plurality of servers, a previously storedrelationship between power consumption efficiency and capacityutilization; obtaining, for each of the plurality of servers, aplurality of current component power consumption values; andcalculating, for each of the plurality of servers, a current powerconsumption efficiency using the plurality of current component powerconsumption values. The method further obtains an amount of capacityutilization necessary to perform an additional workload or job. Themethod then determines, for each of the plurality of servers, apredicted power consumption efficiency by identifying a current capacityutilization that the stored relationship associates with the currentpower consumption efficiency, calculating a predicted capacityutilization value by adding the amount of capacity utilization necessaryto perform the workload to the current capacity utilization, andidentifying the predicted power consumption efficiency that the storedrelationship associates with the predicted capacity utilization value.The method further identifies one of the plurality of servers that hasthe greatest predicted power consumption efficiency, and assigning theworkload to the identified server.

The previously stored relationship between power consumption efficiencyand capacity utilization for each server may, for example, be measuredon an identical server under closely controlled conditions using highprecision sensors and instrumentation. Accordingly, the storedrelationship is expected to provide an accurate representation of howthe server's power consumption efficiency varies with the size and typeof individual jobs or an overall workload made up of multiple jobs.Servers may be identified as being “identical” if they have the samemodel number, or perhaps if they have certain identical components suchas the same processor and same amount of memory. In an ideal situation,each possible configuration of a server would have a previously storedrelationship that is specific to servers having that particularconfiguration. The relationship between power consumption efficiency andcapacity utilization is preferably stored in firmware on each server.

The previously stored relationship may also include data identifying therelationship with other operating conditions that also affect powerconsumption efficiency. In one example, the previously storedrelationship will characterize how power consumption efficiency varieswith capacity utilization and ambient air temperature. Ambient airtemperature is an important condition that affects how much energy theserver must devote to air moving devices, such as fans, in order to keepheat-generating components, such as the processor and memory, atacceptable temperatures. For example, as the ambient air temperaturerises, the fan speeds must be increased and the percentage of totalserver power that is used by the computing components may decrease.Accordingly, embodiments of the present invention may further includemeasuring, for each of the plurality of servers, an ambient airtemperature, wherein the previously stored relationship identifies arelationship between power consumption efficiency and capacityutilization at the measured ambient air temperature.

For each of the plurality of servers, a plurality of current componentpower consumption values are obtained. For example, the plurality ofcurrent component power consumption values may include current powerconsumption values for two or more components selected from a fan, powersupply, voltage regulator, processor, memory, data storage device, andinput output adapter. Current component power consumption values may beobtained for additional types and numbers of components. Withoutlimitation, each server may have one or more of a node manager, abaseboard management controller and an integrated management module forobtaining the plurality of current component power consumption values.

Having obtained current component power consumption values, the methodmay calculate, for each of the plurality of servers, a current powerconsumption efficiency using the plurality of current component powerconsumption values. For example, the current power consumptionefficiency may be calculated as a ratio of total power input to theserver and total power used to perform computing tasks. As used herein,the term “power consumption efficiency” is not limited to one specificmetric or equation. Furthermore, the term “power consumption efficiency”is specifically intended to encompass both the ratio of total powerinput to the server and total power used to perform computing tasks(i.e., (total server power)/(total computing power)) and its inverse(i.e., (total computing power)/(total server power)). The former may berepresented by an Information Technology Usage Effectiveness (ITUE)value that always has a value greater than one, while the latter is amore intuitive value that may be represented as a percentage. Thepresent invention is not limited to any particular representation of apower consumption efficiency. Furthermore, the current power consumptionefficiency may be an instantaneous current power consumption efficiencyor a moving average current power consumption efficiency.

The amount of capacity utilization necessary to perform the workload maybe obtained in various ways. For example, a user may manually enter acapacity utilization value, perhaps based on previous experiences.Alternatively, each server may store a record of capacity utilizationbefore and during performance of the workload each time the workload isperformed. The identity of the workload may be associated withadditional capacity utilization occurring while the workload isperformed. Furthermore, the amount of capacity utilization necessary toperform the workload may be described in terms of various one or moreparameters. For example, capacity utilization may be provided as aprocessor utilization. Furthermore, capacity utilization might beprovided as a combination of a processor utilization and memoryutilization. Any number and type of capacity utilization parameters maybe used.

In one embodiment, the method identifies one of the plurality of serversthat has the greatest predicted power consumption efficiency. Forexample, the method may rank the plurality of servers in order ofpredicted power consumption efficiency, and select the server having thegreatest (most efficiency) predicted power consumption efficiency.Furthermore, the method may identify the plurality of servers from amonga larger network of servers, wherein the plurality of servers each hassufficient available capacity to perform the additional workload.Accordingly, the method may identify a server having the greatestpredicted power consumption efficiency among the plurality of serversthat have sufficient available capacity to perform the additionalworkload. A management server is preferably the entity that identifiesthe server having the greatest predicted power consumption efficiency.

One optional feature that may be included in embodiments of the presentinvention provides for the correction of a measured current powerconsumption efficiency. On the basis that the mass-produced sensors andinstruments that are responsible for measuring component powerconsumption are potentially less accurate than the precision sensors andinstruments used to measure component power consumption in a controlledenvironment, the method may correct or “normalize” the measured currentpower consumption efficiency. Such process may include determining anyoffset between the current power consumption efficiency and thepreviously stored power consumption efficiency at the same capacityutilization and ambient air temperature. The method may then determineone or more correction factors that may be applied to the current powerconsumption efficiency so that the current power consumption efficiencywill match the previously stored power consumption efficiency data. Theone or more correction factors may be applied to the current powerconsumption efficiency before determining a predicted power consumptionefficiency.

Another embodiment of the present invention provides a second method ofassigning workload among servers in a power efficient manner. The methodcomprises accessing, for each of a plurality of servers, a previouslystored relationship between power consumption efficiency, capacityutilization and ambient air temperature; obtaining, for each of theplurality of servers, a plurality of current component power consumptionvalues; and calculating, for each of the plurality of servers, a currentpower consumption efficiency using the plurality of current componentpower consumption values. The method further comprises obtaining anamount of capacity utilization necessary to perform the workload. Stillfurther, the method includes determining, for each of the plurality ofservers, a predicted power consumption efficiency by identifying acurrent capacity utilization that the stored relationship associateswith the current power consumption efficiency, calculating a predictedcapacity utilization value by adding the amount of capacity utilizationnecessary to perform the workload to the current capacity utilization,and identifying the predicted power consumption efficiency that thestored relationship associates with the predicted capacity utilizationvalue. The method further comprises determining, for each of theplurality of servers, any improvement in power consumption efficiencybetween the current power consumption efficiency and the predicted powerconsumption efficiency, identifying one of the plurality of servers thatwould experience the greatest improvement in power consumptionefficiency, and assigning the workload to the identified server.

The foregoing method differs from the previous method primarily in thespecific criteria used to identify or select one of the servers to beassigned the workload. However, both methods are similar in that theyinvolve determining a predicted power consumption efficiency for eachserver as if the server were to be assigned the workload. Accordingly,methods of the present invention are able to make workload assignmentsin view of what impact the assignment would have on power consumptionefficiency of each server. The second method may further include anyadditional aspect or step of the first method, such that each additionalaspect or step will not be described again in the context of the secondmethod.

Yet another embodiment of the present invention provides a computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, wherein the programinstructions are executable by a processor to cause the processor toperform a method. The method comprises accessing, for each of aplurality of servers, a previously stored relationship between powerconsumption efficiency and capacity utilization; obtaining, for each ofthe plurality of servers, a plurality of current component powerconsumption values; and calculating, for each of the plurality ofservers, a current power consumption efficiency using the plurality ofcurrent component power consumption values. The method further obtainsan amount of capacity utilization necessary to perform an additionalworkload or job. The method then determines, for each of the pluralityof servers, a predicted power consumption efficiency by identifying acurrent capacity utilization that the stored relationship associateswith the current power consumption efficiency, calculating a predictedcapacity utilization value by adding the amount of capacity utilizationnecessary to perform the workload to the current capacity utilization,and identifying the predicted power consumption efficiency that thestored relationship associates with the predicted capacity utilizationvalue. The method may then identify one of the plurality of servers thathas the greatest predicted power consumption efficiency, and assigningthe workload to the identified server.

A still further embodiment of the present invention provides a computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, wherein the programinstructions are executable by a processor to cause the processor toperform a second method of assigning workload among servers in a powerefficient manner. The second method comprises accessing, for each of aplurality of servers, a previously stored relationship between powerconsumption efficiency, capacity utilization and ambient airtemperature; obtaining, for each of the plurality of servers, aplurality of current component power consumption values; andcalculating, for each of the plurality of servers, a current powerconsumption efficiency using the plurality of current component powerconsumption values. The method further comprises obtaining an amount ofcapacity utilization necessary to perform the workload. Still further,the method includes determining, for each of the plurality of servers, apredicted power consumption efficiency by identifying a current capacityutilization that the stored relationship associates with the currentpower consumption efficiency, calculating a predicted capacityutilization value by adding the amount of capacity utilization necessaryto perform the workload to the current capacity utilization, andidentifying the predicted power consumption efficiency that the storedrelationship associates with the predicted capacity utilization value.The method further comprises determining, for each of the plurality ofservers, any improvement in power consumption efficiency between thecurrent power consumption efficiency and the predicted power consumptionefficiency, identifying one of the plurality of servers that wouldexperience the greatest improvement in power consumption efficiency, andassigning the workload to the identified server.

The foregoing computer program products may further include computerreadable program code for implementing or initiating any one or moreaspects of the methods described herein. Accordingly, a separatedescription of the methods will not be duplicated in the context of acomputer program product.

FIG. 1 is a graph representing a previously determined and storedrelationship between power consumption efficiency and ambienttemperature for various levels of capacity utilization. In this example,power consumption efficiency of the server is represented as anInformation Technology Usage Effectiveness (ITUE) of the server, and maybe calculated as follows:ITUE=(Total Energy into the Sever)/(Total Energy into the ComputeComponents)In the graph, ITUE (vertical axis) is charted as a function of ambientair temperature (degrees Celsius; see horizontal axis) for a pluralityof different total workload levels (CPU % Maximum Power; see legend).Specifically, the data shown is for a 1U Lenovo server for variousCPU-intensive workloads.

FIG. 2 is a graph of the previously determined and stored relationshipbetween power consumption efficiency and capacity utilization forvarious ambient temperatures. The graph of FIG. 2 is based on the samedata as the graph of FIG. 1, but is presented as ITUE (vertical axis)versus total workload level (CPU % Maximum Power; see horizontal axis)for a plurality of ambient air temperatures (degrees Celsius; seelegend). It should be recognized that the data represented in the graphsof FIG. 1 and FIG. 2 may be stored in a table or other forms.

FIG. 3 is a diagram of a computing system 10 in which a managementserver 20 assigns workloads to a plurality of servers 30 according tovarious embodiments of the present invention. In this non-limitingexample, each server 30 includes a computing components, such as the CPU31, memory controller 32, memory 33, data storage device 34, and inputoutput (I/O) adapter 35. Each server also include various powerdistribution components, such as the power supply unit (PSU) and voltageregulators 40, and various cooling system components, such as one ormore fans 41. Still further, each server 30 includes out-of-bandcomponents or management-side components, such as a node manager 50,baseboard management controller (BMC) 51 (or integrated managementmodule (IMM)), unified extensible firmware interface 52, and an ambientair temperature sensor 53. In the example shown, it is the BMC 51 thatstores the server's power consumption efficiency and capacityutilization relationship data, such as that illustrated in FIGS. 1-2.The server configuration is not limited to the configuration shown, andthere may be differences among the plurality of servers 30. However, forpurposes of this example, it may be assumed that each of the servers 30is identical.

In the illustrated server 30, the node manager 50 is responsible forobtaining power consumption values from the data storage device 34, theI/O adapter 35, the power supply unit and voltage regulators 40, the CPU31, memory controller 32 and memory 33. The baseboard managementcontroller 51 is responsible, in this example, for obtaining powerconsumption values for the fan(s) 41 and the ambient temperaturemeasurement from the ambient temperature sensor 53. Optionally, data maybe polled from one or more of the components using Intelligent PlatformManagement Interface (IPMI) commands. The range of components and powerconsumption values may vary from server to server, and theresponsibilities for obtaining these values may be assigned to one ofmore the node manager 50 and the baseboard management controller (BMC)or integrated management module (IMM) 51. The data thus obtained may beused or stored by the BMC 51 as server efficiency and utilizationrelationship data 54 and/or shared with a management server 20.Optionally, the management server 20 may poll the BMC 51 or node manager50 using IPMI commands.

While each server is illustrated with a node manager and a baseboardmanagement controller (BMC) or integrated management module (IMM), it isbelieved that various sensors, subsystems or components of the servermay be monitored by the node manager, the BMC/IMM or some combinationthereof. For example, it is believed that embodiments of the inventionmay rely upon a node manager to obtain most or all of the performancedata needed for the ITUE calculations, while other embodiments of theinvention may rely upon a BMC/IMM to obtain most or all of theperformance data.

The AC input to the PSU may be considered the total system power, butthe PSU module is inefficient and consumes power. “Output power” is thepower available downstream of the power supply. The output power is theDC power available after the PSU has converted the AC input to DCoutput. Voltage regulators (VRs) typically receive the 12V DC poweroutput of the PSU and step down the voltage to the 5 V, 3.3 V, 1.3 V,etc. DC power used by various subcomponents of the server. A VRefficiency curve identifies the power losses that occur in the voltageregulators, and may be used to identify the distributed power lossesoccurring within the system downstream of the PSU.

If a server component cannot provide a power consumption value directly,alternative values may be obtained and converted into a powerconsumption value. For example, obtaining a fan speed (RPM) of thefan(s) 41 enables a calculation of cooling energy spent by the fan(s) byusing a speed to power consumption correlation. Similarly, a powerconsumption value to a PSU 40 may be determined using a PSU efficiencycurve or data and the input power to the PSU 40. Similarly, a voltageregulator (VR) efficiency curve or data may be used to determine thepower consumption of the voltage regulators from the PSU output power(i.e., input power to the voltage regulators).

The management server 20 includes a workload placement manager 22, whichis an application program that receives workload requests 60 andidentifies one of the servers 30 to which the workload will be assigned.To facilitate this decision, the management server 20 may store serverdata 24 that is used in the power consumption efficiency calculations.Optionally, the server data 24 may include any or all of the serverefficiency and utilization relationship data 54 from each of the servers30. The data 54, 24 may be the raw component power consumption values ora power consumption efficiency, such as ITUE, calculated from the rawcomponent power consumption values. For example, the ITUE (InformationTechnology Usage Effectiveness) of each server can be calculated at agiven point in time for any workload, using the equation:ITUE=(Total Server Power)/(Total Server Power−PSU losses−PowerDistribution losses−Cooling system losses)

The computing system 10 is not limited to any particular topology andmay be applied in various types of data centers, such as a virtualizedserver farm or a high performance computing (HPC) data center.Furthermore, the methods of the present invention may be equallyapplicable to homogenous and heterogeneous server populations. Thealgorithm will rate the servers based on a predicted energy efficiencyof the server for a given workload and helps the workload placementmanager to identify the most efficient server before placing a newworkload or moving workload when needed.

It should be recognized that access to the power consumption ofadditional server subsystems, such as memory, I/O and data storage,enables a more detailed determination of how these subsystems affectchanges in server power efficiency. Subsystem loading, especially theCPU, will often dictate fan speeds, which clearly effects systemefficiency. The objective is to understand the cause and effectrelationship between loading and fan speed response, which can be thencorrelated to the in-situ run-time efficiency value as calculated above.Accordingly, embodiments of the present invention may monitor powerconsumption of other subsystems, such as the memory and data storagedevices, and treat that data in the same manner as the CPU powerconsumption.

FIG. 4 is a flowchart of a method 70 of assigning workload among serversin a power-efficient manner according to one embodiment of the presentinvention. Such a method may be performed collectively by the nodemanager 50, the BMC 51, and the workload placement manager 22 (see FIG.3). In step 71, the method accesses, for each of a plurality of servers,a previously stored relationship between power consumption efficiencyand capacity utilization. In step 72, the method obtains, for each ofthe plurality of servers, a plurality of current component powerconsumption values. In step 73, the method calculates, for each of theplurality of servers, a current power consumption efficiency using theplurality of current component power consumption values. The methodthen, in step 74, obtains an amount of capacity utilization necessary toperform an additional workload. In step 75, the method determines, foreach of the plurality of servers, a predicted power consumptionefficiency by identifying a current capacity utilization that the storedrelationship associates with the current power consumption efficiency,calculating a predicted capacity utilization value by adding the amountof capacity utilization necessary to perform the workload to the currentcapacity utilization, and identifying the predicted power consumptionefficiency that the stored relationship associates with the predictedcapacity utilization value. Step 76 identifies one of the plurality ofservers that has the greatest predicted power consumption efficiency,and step 77 assigning the workload to the identified server.

FIG. 5 includes graphs of the previously stored power consumptionefficiency relationships (see FIGS. 1 and 2) for each of three servershaving sufficient capabilities and current availability to perform anadditional workload. As shown, each server is currently operating at adifferent level of capacity utilization. For the purpose of discussingthe three graphs, it may be assumed that the three servers (Server #1,Server #2 and Server #3) are identical and have the same powerconsumption efficiency relationship. In other words, for any givenambient air temperature, each of the servers has the same relationshipbetween power consumption efficiency (vertical axis) and capacityutilization (horizontal axis).

For each server, a method accesses, for each of the three servers(Server #1, Server #2 and Server #3), a previously stored relationshipbetween power consumption efficiency and capacity utilization. This isshown by the solid curved lines (80-1, 80-2 and 80-3) in each graph. Themethod obtains, for each of the servers, a plurality of currentcomponent power consumption values and calculates a current powerconsumption efficiency (82-1, 82-2 and 82-3, respectively) using theplurality of current component power consumption values. The method thenobtains an amount of capacity utilization necessary to perform anadditional workload or job, where the additional capacity utilizationassociated with the additional job is a fixed distance along thehorizontal axis (see identical arrow 84 in each graph). The method maythen determine, for each of the plurality of servers, a predicted powerconsumption efficiency (86-1, 86-2 and 86-3, respectively) byidentifying a current capacity utilization (83-1, 83-2 and 83-3,respectively) that the stored relationship associates with the currentpower consumption efficiency, calculating a predicted capacityutilization value (85-1, 85-2 and 85-3, respectively) by adding theamount of capacity utilization 84 necessary to perform the workload tothe current capacity utilization, and identifying the predicted powerconsumption efficiency (86-1, 86-2 and 86-3, respectively) that thestored relationship (80-1, 80-2 and 80-3, respectively) associates withthe predicted capacity utilization value. The method then assigns theadditional workload to the server identified as having the greatestpredicted power consumption efficiency.

Even with three identical servers having the same stored relationshipbetween power consumption efficiency and capacity utilization, the factthat the servers have different current capacity utilization valuesmeans that the servers will have different predicted power consumptionefficiency (86-1, 86-2 and 86-3, respectively). As shown, Server #1 hasthe lowest capacity utilization and would experience the greatestefficiency improvement as a result of being assigned the additionalworkload. By contrast, Server #3 has the greatest capacity utilizationand would achieve the greatest power consumption efficiency (i.e.,lowest ITUE) as a result of being assigned the additional workload. Itshould be appreciated that the methods of the present invention areequally applicable to servers that are not identical.

In one embodiment, the method enables user selection of a workloadassignment mode, perhaps using a graphical user interface to the BMC/IMMor the workload placement manager. In a first workload assignment mode,the additional workload may be assigned to the server that is identifiedas performing the workload with the greatest efficiency. In theforegoing example, this would be Server #3. Accordingly, less efficientservers may enter a sleep state resulting in power savings.

In a second workload assignment mode, the workload is assigned to theserver that is identified as having the greatest efficiency improvement.Accordingly, an overall ITUE for the data center will also improve. Inthe foregoing example, this second workload assignment mode results inassignment of the workload to Server #1.

FIG. 6 is a graph illustrating current power consumption efficiencymeasurements compared to the previously stored power consumptionefficiency relationship 80 under three identical workload conditions.Current ITUE values calculated on the basis of measured powerconsumption values for various components of a server may be normalizedwith respect a predetermined ITUE curve 80 stored by the server. Thepredetermined ITUE data or curve(s) 80 has been determined fromcontrolled laboratory measurements of server performance under variousworkloads (A, B and C) and conditions, and is stored in the serverbefore shipment or installation. The predetermined ITUE data 80 isbelieved to be more accurate than the actual ITUE calculated by theserver during actual use, primarily due to possible sensor inaccuracies,measurement errors, or variations in installation details. Therefore,actual ITUE measurements (A′, B′ and C′) made by the deployed serverunder the same workloads and conditions are compared to thepredetermined ITUE data. Any offset between the measured ITUE value (A′,B′ and C′) and the predetermined ITUE values (A, B and C), under similarworkload and conditions, may be determined and one or more correctionfactors may be used so that the measured ITUE values (A′, B′ and C′) aremade to match the predetermined ITUE values (A, B and C). Thisnormalization of the actual ITUE is believed to improve the accuracy ofa method to identify the server that will perform a workload mostefficiently.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing. Computer program code for carrying out operations foraspects of the present invention may be written in any combination ofone or more programming languages, including an object orientedprogramming language such as Java, Smalltalk, C++ or the like andconventional) procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

Aspects of the present invention may be described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, and/or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,components and/or groups, but do not preclude the presence or additionof one or more other features, integers, steps, operations, elements,components, and/or groups thereof. The terms “preferably,” “preferred,”“prefer,” “optionally,” “may,” and similar terms are used to indicatethat an item, condition or step being referred to is an optional (notrequired) feature of the invention.

The corresponding structures, materials, acts, and equivalents of allmeans or steps plus function elements in the claims below are intendedto include any structure, material, or act for performing the functionin combination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but it is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method, comprising: accessing, for each of aplurality of servers, a previously stored relationship between powerconsumption efficiency and capacity utilization; obtaining, for each ofthe plurality of servers, a plurality of current server component powerconsumption values; calculating, for each of the plurality of servers, acurrent power consumption efficiency using the plurality of currentserver component power consumption values; obtaining an amount ofcapacity utilization necessary to perform an additional workload;determining, for each of the plurality of servers, a predicted powerconsumption efficiency by identifying a current capacity utilizationthat the stored relationship associates with the current powerconsumption efficiency, calculating a predicted capacity utilizationvalue by adding the amount of capacity utilization necessary to performthe additional workload to the current capacity utilization, andidentifying the predicted power consumption efficiency that the storedrelationship associates with the predicted capacity utilization value;determining, for each of the plurality of servers, any improvement inpower consumption efficiency between the current power consumptionefficiency and the predicted power consumption efficiency; identifyingone of the plurality of servers that would experience the greatestimprovement in power consumption efficiency; and assigning the workloadto the identified server.
 2. The method of claim 1, wherein the capacityutilization is a processor utilization.
 3. The method of claim 1,wherein the previously stored relationship for each server is stored infirmware on each server.
 4. The method of claim 1, wherein the currentpower consumption efficiency is calculated as a ratio of total powerinput to the server and total power used to perform computing tasks. 5.The method of claim 1, wherein the current power consumption efficiencyis an instantaneous current power consumption efficiency.
 6. The methodof claim 1, wherein the current power consumption efficiency is a movingaverage current power consumption efficiency.
 7. The method of claim 1,further comprising: identifying the plurality of servers from among alarger network of servers, wherein the plurality of servers each hassufficient available capacity to perform the additional workload.
 8. Themethod of claim 1, wherein the plurality of current component powerconsumption values include current power consumption values for two ormore components selected from a fan, power supply, voltage regulator,processor, memory, data storage device, and input output adapter.
 9. Themethod of claim 1, wherein a management server identifies the serverthat would experience the greatest improvement in power consumptionefficiency.
 10. The method of claim 1, wherein accessing, for each of aplurality of servers, a previously stored relationship between powerconsumption efficiency and capacity utilization, includes accessing, foreach of the plurality of servers, a previously stored relationshipbetween power consumption efficiency, capacity utilization and ambientair temperature.
 11. The method of claim 10, further comprising:measuring, for each of the plurality of servers, an ambient airtemperature, wherein the previously stored relationship identifies arelationship between power consumption efficiency and capacityutilization at the measured ambient air temperature.
 12. The method ofclaim 1, wherein each server has one or more of a node manager, abaseboard management controller and an integrated management module forobtaining the plurality of current server component power consumptionvalues.
 13. The method of claim 1, wherein obtaining an amount ofcapacity utilization necessary to perform the additional workload,includes storing a record of capacity utilization before and duringperformance of the workload each time the workload is performed.
 14. Acomputer program product comprising non-transitory computer readablestorage media having program instructions embodied therewith, theprogram instructions executable by a processor to: access, for each of aplurality of servers, a previously stored relationship between powerconsumption efficiency and capacity utilization; obtain, for each of theplurality of servers, a plurality of current server component powerconsumption values; calculate, for each of the plurality of servers, acurrent power consumption efficiency using the plurality of currentserver component power consumption values; obtain an amount of capacityutilization necessary to perform an additional workload; determine, foreach of the plurality of servers, a predicted power consumptionefficiency by identifying a current capacity utilization that the storedrelationship associates with the current power consumption efficiency,calculating a predicted capacity utilization value by adding the amountof capacity utilization necessary to perform the additional workload tothe current capacity utilization, and identifying the predicted powerconsumption efficiency that the stored relationship associates with thepredicted capacity utilization value; determine, for each of theplurality of servers, any improvement in power consumption efficiencybetween the current power consumption efficiency and the predicted powerconsumption efficiency; identify one of the plurality of servers thatwould experience the greatest improvement in power consumptionefficiency; and assign the workload to the identified server.
 15. Thecomputer program product of claim 14, wherein the capacity utilizationis a processor utilization.
 16. The computer program product of claim14, wherein the current power consumption efficiency is calculated as aratio of total power input to the server and total power used to performcomputing tasks.
 17. The computer program product of claim 14, whereinthe program instructions are further executable by the processor to:identify the plurality of servers from among a larger network ofservers, wherein the plurality of servers each has sufficient availablecapacity to perform the additional workload.
 18. The computer programproduct of claim 14, wherein the program instructions executable by theprocessor to access, for each of a plurality of servers, a previouslystored relationship between power consumption efficiency and capacityutilization, include the program instructions executable by theprocessor to access, for each of the plurality of servers, a previouslystored relationship between power consumption efficiency, capacityutilization and ambient air temperature.
 19. The computer programproduct of claim 18, wherein the program instructions are furtherexecutable by the processor to: measure, for each of the plurality ofservers, an ambient air temperature, wherein the previously storedrelationship identifies a relationship between power consumptionefficiency and capacity utilization at the measured ambient airtemperature.
 20. The computer program product of claim 14, wherein theprogram instructions executable by the processor to obtain an amount ofcapacity utilization necessary to perform the additional workload,include program instructions executable by the processor to store arecord of capacity utilization before and during performance of theworkload each time the workload is performed.